遺傳方差參數 的英文怎麼說

中文拼音 [yízhuànfāngchāshēnshǔ]
遺傳方差參數 英文
genetic variance parameters
  • 遺 : 遺動詞[書面語] （贈與） offer as a gift; make a present of sth : 遺之千金 present sb with a gener...
  • 傳 : 傳名詞1 （解釋經文的著作） commentaries on classics 2 （傳記） biography 3 （敘述歷史故事的作品）...
  • 方 : Ⅰ名詞1 （方形; 方體） square 2 [數學] （乘方） involution; power 3 （方向） direction 4 （方面） ...
  • 差 : 差Ⅰ名詞1 （不相同; 不相合） difference; dissimilarity 2 （差錯） mistake 3 [數學] （差數） differ...
  • 參 : 參構詞成分。
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 遺傳 : [生物學] heredity; hereditary; inheritance; inherit
  • 方差 : dispersion
  1. Three genetic coefficients, including maximum phyllochron, elongation internode number and plant height, were used to describe the genetic differences in leaf blade and internode among different wheat varieties

    模型引入3個品種參數，即最大葉熱間距、伸長節間數和株高，分別反映了不同小麥品種在葉片和節間等方面的遺傳差異性。
  2. It is very important to estimate the basic parameters in helicopter preliminary design. neural network ( nn ) has the advantages in estimating accuracy and generalization over traditional methods. however, there are some difficulties in using nn, e. g., how to select a proper network structure and the number of hidden layers. in this paper, structure and connection weight of a three - layer nn are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. the proposed method can not only give an optimal nn structure and connection weight, but also reduce the prediction error and has the capability of self - learning when the latest data are available. furthermore, this method can be easily applied to helicopter design systems

    在直升機初步設計階段估算其基本參數是很重要的.神經網路的通用性和精度比傳統的估算方法有更多的優勢，但是在應用神經網路時存在如何選擇合適的網路結構和隱層節點數目等一些困難.應用遺傳演算法優化三層神經網路結構和連接權重，並將優化得到的網路應用於直升機參數選擇中.該方法不但可以給出一個最優的神經網路結構和連接權重，而且降低了估算誤差，具有及時應用最新數據學習的能力.此外，該方法易於在直升機設計系統中得到應用
  3. The ga ( genetic algorithm ) is applied to solve the optimal parameters of the mtmd appended in cabin in frequency - domain. 4. the dynamic responses of the cabin with different mtmd are compared under different wind speed

    以基於davenport風速譜的位移諧響應根方差為目標函數，應用遺傳演算法，在頻域上獲得了mtmd的最優參數值。
  4. In this method, ga is used to optimize connection weights of forward - back neural network until the learning error has tended to stability, then we use sp algorithm with optimized weights to finish short - term load forecasting process

    我們用遺傳演算法來訓練網路參數，直到誤差趨於一穩定值，然後用優化的權值進行bp演算法，實現短期負荷預測，模擬實驗結果表明該方法加快網路學習速度，並能提高負荷預測精度。
  5. Abstract : it is very important to estimate the basic parameters in helicopter preliminary design. neural network ( nn ) has the advantages in estimating accuracy and generalization over traditional methods. however, there are some difficulties in using nn, e. g., how to select a proper network structure and the number of hidden layers. in this paper, structure and connection weight of a three - layer nn are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. the proposed method can not only give an optimal nn structure and connection weight, but also reduce the prediction error and has the capability of self - learning when the latest data are available. furthermore, this method can be easily applied to helicopter design systems

    文摘:在直升機初步設計階段估算其基本參數是很重要的.神經網路的通用性和精度比傳統的估算方法有更多的優勢，但是在應用神經網路時存在如何選擇合適的網路結構和隱層節點數目等一些困難.應用遺傳演算法優化三層神經網路結構和連接權重，並將優化得到的網路應用於直升機參數選擇中.該方法不但可以給出一個最優的神經網路結構和連接權重，而且降低了估算誤差，具有及時應用最新數據學習的能力.此外，該方法易於在直升機設計系統中得到應用
  6. The dual standard quantity ( the work piece and the discrete standard quantity ) mutual measuring and model verification methods are also proposed, which perfects the whole modifying process from data measuring, error separation, model establishment to real correction. after researching the discrete standard quantity system dynamic error separation technique, two error correction methods based on genetic algorithm and neural network mixed modeling technique are established. the two methods are the discrete standard quantity dynamic error direct / synchronous correction and prediction model correction ; the model ' s parameters and model ' s exercising method are also confirmed

    設計了雙標準量值（工件和離散標準量）互比測量的模型驗證方法，完善了從數據測量、誤差分離、模型建立到實際修正的整個修正過程；研究了離散標準量系統動態誤差分離技術，建立了基於遺傳進化演算法與神經網路混合建模技術的兩種誤差修正方法? ?離散標準量動態誤差直接（同步）修正方法和預報模型修正方法，並確定了模型結構參數和模型訓練方法；分析了預報模型的多次預報性質，並得出了多次預報與多步預報的等效關系，確定了測量系統的有效預報范圍以及模型參數對泛化誤差的影響；進行了模型的對比實驗驗證和被測工件動態誤差修正試驗，成功地實現了任意二面角和圓分度的實時誤差修正。
  7. The estimation procedure is based on the steady - state characteristics versus slip curves. the sum of squares of differences between calculated and experimental characteristics is employed as the fitting criterion. the machine parameters are obtained by minimizing the least - squares cost function using genetic algorithms

    該方法利用電機穩態特性曲線，以特性計算值和實驗值的擬合誤差的平方和作為判斷準則，通過應用遺傳演算法最小化二乘準則函數來獲取電機參數。
  8. In order to design wavelength insensitive power splitter based on soi, the wavelength characteristic of mach - zehnder interferometer were investigated by means of bpm and fdm ( finite - difference method ). then the parameters of mach - zehnder interferometer were optimum designed using genetic algorithm. besides, genetic algorithm is also used to optimize the upper tier parameters of mzi based silica - on - silicon to attain a flat spectral response

    為了設計對波長不敏感的soi材料的mz功分器，利用束傳播法（ bpm ）和有限差分模式解方法（ fdm ）對mach - zehnder結構的組成部分定向耦合器和相位延遲部分做了波長相關特性的計算，然後通過遺傳演算法優化設計了mach - zehnder的結構參數。
  9. On the base of brief introduction simple theory of genetic algorithms, we discuss the value of each genetic operator and the means of genetic operation. correct scheme of genetic algorithms is given out according to the special bolted sphere node. the genetic algorithms optimal program is compiled in the environment of matlab using the method of transferring parameters among the main function and the sub - function

    為此，首先分析了螺栓節點球的球心誤差，並通過矩陣變換理論給出計算球心誤差的公式，進一步討論了自動分度角度的計算方法；在簡介遺傳演算法的基本原理的基礎上，對各遺傳運算元的取值及遺傳操作方法進行了討論，針對典型的螺栓節點球零件給出了具體的遺傳演算法執行方案；通過matlab軟體編程環境進行程序的編制，利用主函數和子函數之間參數傳遞的方法實現了遺傳演算法優化，並且編制了圖形用戶界面，對優化結果採用圖文結合的形式輸出。
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